import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(MultiHeadAttention, self).__init__()
        # 这里仅展示初始化过程的一部分，省略了权重和偏置的初始化
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads

        # 通常还会包含权重和偏置的初始化，这里省略

    def forward(self, values, keys, query, mask):
        # 这里仅展示函数签名，省略了实际的注意力计算过程
        # 实际的实现需要考虑缩放点积注意力、多头注意力机制以及mask的应用
        pass

class TransformerEncoderLayer(nn.Module):
    def __init__(self, embed_size, heads, forward_expansion, dropout):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(embed_size, heads)
        self.norm1 = nn.LayerNorm(embed_size)
        self.feed_forward = nn.Sequential(
            nn.Linear(embed_size, forward_expansion * embed_size),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(forward_expansion * embed_size, embed_size)
        )
        self.norm2 = nn.LayerNorm(embed_size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src):
        # 注意：这里省略了mask参数，但在实际应用中可能需要处理mask
        src2 = self.self_attn(src, src, src)  # 这里假设self_attn已经实现了必要的逻辑
        src = src + self.dropout(src2)
        src = self.norm1(src)
        src2 = self.feed_forward(src)
        src = src + self.dropout(src2)
        src = self.norm2(src)
        return src

class TransformerEncoder(nn.Module):
    def __init__(self, embed_size, num_layers, heads, forward_expansion, dropout):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList([TransformerEncoderLayer(embed_size, heads, forward_expansion, dropout)
                                    for _ in range(num_layers)])
        self.norm = nn.LayerNorm(embed_size)

    def forward(self, src):
        output = src
        for layer in self.layers:
            output = layer(output)
        return self.norm(output)

# 示例实例化
if __name__ == "__main__":
    embed_size = 512
    num_layers = 6
    heads = 8
    forward_expansion = 4
    dropout = 0.1

    model = TransformerEncoder(embed_size, num_layers, heads, forward_expansion, dropout)
    # 注意：这里没有提供输入数据的具体形状和类型，仅展示了模型结构
    # 在实际应用中，你需要根据任务需求准备相应的输入数据
